Comparar métodos

	Mínimos Cuadrados Ponderados Robustos (Robust WLS)	Regresión por Mínimos Cuadrados Ordinarios (MCO)	Regresión Cuantílica
Campo	Econometría	Econometría	Econometría
Familia	Regression model	Regression model	Regression model
Año de origen	1964/1981	2019	1978
Autor original	Huber, P. J.	Wooldridge (textbook treatment); classical least squares	Koenker & Bassett
Tipo	Robust weighted regression	Linear regression	Conditional quantile regression
Fuente seminal	Huber, P. J. (1981). Robust Statistics. Wiley. ISBN: 978-0471418054	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Alias	robust weighted least squares, RWLS, heteroscedasticity-robust WLS, outlier-robust weighted regression	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	conditional quantile regression, regression quantiles, Kantil Regresyon
Relacionados	5	5	5
Resumen	Robust WLS combines weighted least squares — which corrects for known or estimated heteroscedasticity — with robust M-estimation that down-weights influential outliers. The result is a regression estimator that is simultaneously efficient under non-constant error variance and resistant to observations that would otherwise distort coefficient estimates.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.